mirror of
https://github.com/TheAlgorithms/Python.git
synced 2024-10-07 06:09:28 +00:00
197 lines
45 KiB
Plaintext
197 lines
45 KiB
Plaintext
|
{
|
||
|
|
"cells": [
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 1,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"C:\\Users\\Satyam\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
|
||
|
|
" from numpy.core.umath_tests import inner1d\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Importing the libraries\n",
|
||
|
|
"import numpy as np\n",
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"import pandas as pd\n",
|
||
|
|
"from sklearn.model_selection import train_test_split\n",
|
||
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
||
|
|
"from sklearn.metrics import confusion_matrix\n",
|
||
|
|
"from matplotlib.colors import ListedColormap\n",
|
||
|
|
"from sklearn.ensemble import RandomForestClassifier"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 2,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Importing the dataset\n",
|
||
|
|
"dataset = pd.read_csv('Social_Network_Ads.csv')\n",
|
||
|
|
"X = dataset.iloc[:, [2, 3]].values\n",
|
||
|
|
"y = dataset.iloc[:, 4].values"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 3,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Splitting the dataset into the Training set and Test set\n",
|
||
|
|
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"C:\\Users\\Satyam\\AppData\\Roaming\\Python\\Python35\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
|
||
|
|
" warnings.warn(msg, DataConversionWarning)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Feature Scaling\n",
|
||
|
|
"sc = StandardScaler()\n",
|
||
|
|
"X_train = sc.fit_transform(X_train)\n",
|
||
|
|
"X_test = sc.transform(X_test)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[[63 5]\n",
|
||
|
|
" [ 3 29]]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Fitting classifier to the Training set\n",
|
||
|
|
"# Create your classifier here\n",
|
||
|
|
"classifier = RandomForestClassifier(n_estimators=10,criterion='entropy',random_state=0)\n",
|
||
|
|
"classifier.fit(X_train,y_train)\n",
|
||
|
|
"# Predicting the Test set results\n",
|
||
|
|
"y_pred = classifier.predict(X_test)\n",
|
||
|
|
"\n",
|
||
|
|
"# Making the Confusion Matrix\n",
|
||
|
|
"cm = confusion_matrix(y_test, y_pred)\n",
|
||
|
|
"print(cm)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXuYHGWV8H+nZ5JJSGISBsgFCMl8kiEKGgTRIHyJIIgX\nFhV1wairLkbddVXQ9ZZlvaxZddeV9bJ+bgR1lSwoImoQVIhMBI0gYDRiQsAAAZJMyECGTEg6mZnz\n/VHVmb681VM1VdVVPXN+z5Mn3dXVVeft7jnnfc857zmiqhiGYRhGIWsBDMMwjHxgBsEwDMMAzCAY\nhmEYPmYQDMMwDMAMgmEYhuFjBsEwDMMAzCCMCURkiYg8lrUczULan5eIfF1ELi97/h4R6RaRPhFp\n9//vSPB+R4rIJhGZmNQ1q65/v4icmfS5WSAed4vICVnLkgVmEDJCRB4WkX3+H/8OEfm2iEzOWq64\niIiKyF5/XH0isrvB9w+lzEXkNBG5SUR2i8iTInKXiLy9ETKq6rtV9V98OcYBXwTOVdXJqtrj/78l\nwVt+FPi2qu4TkfvKvpsBEdlf9vzjIxxPp6renvS5jUBErhaRT5aeq7cx64vApzITKkPMIGTL+ao6\nGVgInAx8LGN5kuL5vlKbrKrTor5ZRFrTEKrs+ouAXwJrgWcD7cB7gFeked8AZgATgPviXsj1uYlI\nG/A3wNUAqvrc0ncD3A68t+y7+tcw1xwD/Ag4V0SOylqQRmMGIQeo6g7g53iGAQAReZWI/F5EnhaR\nR8tnMSIy15+J/42IbBWRXSKyvOz1if6K4ykR+TPwwvL7icgCEenyZ8f3ichflb32bRH5mojc7M8a\nfy0iM0XkP/3rbRKRk0cyThF5p4g86M/IfyIis8teUxH5exF5AHjAP3aCiNzin3+/iLyx7PxXisif\nRWSPiDwuIh8SkUnAzcDsslnv7BpB4N+B/1HVz6vqLvW4R1Xf6DgXEfmoiPzFv9efReS1Za89W0TW\nikiv/z18zz8uInKFiOz0v8MNInJi2Wf8GRGZD9zvX2q3iPyy7LN4tv+4TUS+4H/P3eK5myb6ry0R\nkcdE5CMisgP4lkP8FwG7VTWUC0xELhGRX4nIl0XkSeCfROR4EbnN/x52ich3RWRq2XseE5El/uPP\niMg1/sx7j4j8SUReMMJzTxWR9f5r14rIdeV/B1Vyz/flLn0P/1v22nNE5FZf/k0icqF//O+AvwY+\n7v9WbgBQ1WeA9cA5YT6zUYWq2r8M/gEPAy/zHx8DbAC+VPb6EuAkPKP9PKAbeI3/2lxAgW8AE4Hn\nA0Vggf/65/Bmf4cDxwJ/Ah7zXxsHPAh8HBgPnAXsATr9178N7AJOwZu5/hJ4CHgr0AJ8BritzrgU\neLbj+Fn+dV8AtAFfAX5V9b5bfJknApOAR4G3A614K6hdwHP887cDZ/qPpwMvKPvcHqsj32HAAPDS\nOudUXAN4AzDb/y7+GtgLzPJfuwZY7r82ATjDP/5y4B5gGiDAgrL3fBv4TNV32er6DIErgJ/4n8sU\nYDXw2TI5+4HP+5/pRMdY/h74acA4u4BLqo5d4l/zPf73PRGYD5zt/16OAn4NfKHsPY8BS/zHnwH2\n+eNvwTO+d0Q91x/PY8B78X6zbwAOAp8MGMt1wEfKvoeX+McnA4/j/X5b8X7XPQz93q92XRP4GvBv\nWeuJRv+zFUK2/EhE9uApvp3AJ0ovqGqXqm5Q1UFV/SOe4llc9f5Pqeo+Vf0D8Ac8wwDwRmCFqj6p\nqo8CXy57z4vx/kg+p6oHVPWXwI3AxWXn3KDejHk/cAOwX1W/o6oDwPfwlHM97vVXH7tFpHTvpcA3\nVfVeVS3iuccWicjcsvd91pd5H/Bq4GFV/Zaq9qvq74Hr8RQDeMrhOSLyLFV9SlXvHUamEtPxlMb2\nkOejqtep6jb/u/ge3grmtDI5jgNmq+p+Vb2j7PgU4ARAVHWjqoa+J3irDGAZcKn/uewB/hW4qOy0\nQeATqlr0P7dqpuEZ/ChsVdX/p6oD/u9rs6qu8X8vO/GMVPVvsZy1qvpz//fyXcpWvhHOfQkwqKpf\nVdWDqnodnoEN4iCecZ3lfw+/9o9fAGz2f7/9qnoPnkvo9cN8BnvwPrsxhRmEbHmNqk7Bm+mdABxR\nekFEXuQv058QkV7g3eWv++woe/wMnqIHbzb7aNlrj5Q9ng08qqqDVa8fXfa8u+zxPsfz4YLfL1DV\naf6/95Xd95AcqtqHN1Mrv2+5zMcBLyozLLvxjMpM//ULgVcCj/gum0XDyFTiKTwlOivk+YjIW33X\nRUmOExn6Lj6MtwK4Szz32zv88f0S+CrwX8BOEVkpIs8Ke0+fI/FWNPeU3ftn/vEST/iGO4in8AxT\nFMq/B8RzGX7fd809jbfCqf4tllP9u5w0gnNn460QAuWq4oN4K4m7fffc3/jHjwNeUvU7+muG//6n\nAA1NiMgDZhBygKquxfsj+0LZ4f/FcxUcq6pTga/jKZ4wbMdzFZWYU/Z4G3CsiBSqXn88othR2Yb3\nxwmA7+9vr7pveendR/Fmj9PK/k1W1fcAqOrvVPUCPBfGj4DvO65Rg3r+4XV4BmVYROQ4PNfce4F2\n9YLkf8L/LlR1h6q+U1VnA+8Cvlby/6vql1X1FOA5eG6XfwxzzzJ24Rng55Z9BlPVCwgfGtIw1/ij\nf+8oVF/z83guyZNU9VnA2wj/Wxwp26mcLEDlb7oCVd2uqpeo6iw8N9lKEZmH9zta4/gdvbf01oBL\nLsBbdY8pzCDkh/8EzhGRkttnCvCkqu4XkdOAN0W41veBj4nIdBE5BviHstfuxJuJfVhExvkBvvOB\na2OPoD7XAG8XkYXiZb78K3Cnqj4ccP6NwHwReYsv5zgReaF4AfHxIrJURKaq6kHgabxZP3irmfby\noKeDDwNvE5F/FJF2ABF5voi4PoNJeErjCf+8t+OtEPCfv8H/jMGbjSsw6Mv6IvHSSvcC+8tkDIW/\nivsGcIX4GS8icrSIvDzCZe4CpolItXKNwhS8MfSKyLHAh2JcKyx3AK3i7dFo9QPBpwSdLCJvLBvj\nbrzvYQBvUvVcEXlT2e/oNBHp9M/tBjqqrjURz3V1a8Jjyj1mEHKCqj4BfAf4Z//Q3wGf9mMM/8zQ\nDDgMn8JzzzwE/ALPN1u6zwE8A/AKvBno14C3quqmuGOoh6reClyOFwfYDvwfKn3h1efvAc71z9mG\n51ooBU8B3gI87Lsw3o3nTsIfxzXAFt9FUJNlpKq/wQtyn+Wf9ySwErjJce6fgf/AW1V04wX6f112\nyguBO0WkD0/5vF+9PQTPwlPmT+F9Fz14QdOofAQvCeC3/lhvBTrrv6VC/gN4q883j+DeJT6BFzPp\nxRvj9TGuFQo/zvRavO/2Kby42E14KxUXLwJ+JyJ7gR8Cf6+qW1W1Fy9o/Wa8390O4LMM/Y6uBJ4v\nXgbdD/xjrwFuUdVuxhiiag1yDGM0IyJH4mWdnRwQeG4KROQe4D9V9bvDnjzyewjwO+Atqroxrfvk\nFTMIhmHkEt+duRFvdfU3eNly8/xMJyMFxuIuRMMwmoMFeGnOk4C/ABeaMUgXWyEYhmEYgAWVDcMw\nDJ+mchmNmzJOJxwxIWsxDGPU0Ffs45Q9yRbZvWdKHy2FFiaOS6XatjEC+h7u26WqRw53XlMZhAlH\nTODUT56atRiGMWpY+1AXd69N9m9q3JldTJ40hYUz61WsMBpJ19u6Hhn+LHMZGYZhGD5mEAzDMAzA\nDIJhGIbh01QxBMMwjCyY3DKZi+ZcxKyJsyjkdB49yCDb923n2q3X0jfQN6JrmEEwDMMYhovmXMSJ\nx5xI25Q2vOoW+UNVad/TzkVcxJUPXTmia+TT1BmGYeSIWRNn5doYAIgIbVPamDUxdKuPGswgGIZh\nDEOBQq6NQQkRieXSyswgiMgEEblLRP7gd5r6VFayGIZhGNmuEIrAWar6fLxmFOeJyIszlMcwDCPX\n3L7mds578Xmc+8JzWfm
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x14150b50>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3lqTO0wuehm5+zbgZuC
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x14717ff0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Visualising the Training set results\n",
|
||
|
|
"X_set, y_set = X_train, y_train\n",
|
||
|
|
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
|
||
|
|
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
|
||
|
|
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
|
||
|
|
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
|
||
|
|
"plt.xlim(X1.min(), X1.max())\n",
|
||
|
|
"plt.ylim(X2.min(), X2.max())\n",
|
||
|
|
"for i, j in enumerate(np.unique(y_set)):\n",
|
||
|
|
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
|
||
|
|
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
|
||
|
|
"plt.title('Random Forest Classifier (Training set)')\n",
|
||
|
|
"plt.xlabel('Age')\n",
|
||
|
|
"plt.ylabel('Estimated Salary')\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"# Visualising the Test set results\n",
|
||
|
|
"X_set, y_set = X_test, y_test\n",
|
||
|
|
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
|
||
|
|
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
|
||
|
|
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
|
||
|
|
" alpha = 0.75, cmap = ListedColormap(('red', 'green')))\n",
|
||
|
|
"plt.xlim(X1.min(), X1.max())\n",
|
||
|
|
"plt.ylim(X2.min(), X2.max())\n",
|
||
|
|
"for i, j in enumerate(np.unique(y_set)):\n",
|
||
|
|
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
|
||
|
|
" c = ListedColormap(('red', 'green'))(i), label = j)\n",
|
||
|
|
"plt.title('Random Forest Classifier (Test set)')\n",
|
||
|
|
"plt.xlabel('Age')\n",
|
||
|
|
"plt.ylabel('Estimated Salary')\n",
|
||
|
|
"plt.legend()\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.5.1"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|